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IPPM Working Group                                           M. Cociglio
Internet-Draft                                            Telecom Italia
Intended status: Experimental                                   C. Corbo
Expires: September 10, 2020                        Politecnico di Torino
                                                             G. Fioccola
                                                     Huawei Technologies
                                                                 M. Nilo
                                                          Telecom Italia
                                                                R. Sisto
                                                   Politecnico di Torino
                                                           March 9, 2020


     The Big Data Approach for Multipoint Alternate Marking method
                  draft-c2f-ippm-big-data-alt-mark-00

Abstract

   This document introduces a new approach for the Alternate Marking
   method.  It is called Big Data Multipoint Alternate Marking method
   and, starting from the methodology described in RFC 8321 and draft-
   ietf-ippm-multipoint-alt-mark, it explains how to implement
   performance measurement analytics on the Network Management System by
   analysing the raw data of the network nodes.

Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC 2119 [RFC2119].

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at https://datatracker.ietf.org/drafts/current/.

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on September 10, 2020.




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Copyright Notice

   Copyright (c) 2020 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (https://trustee.ietf.org/license-info) in effect on the date of
   publication of this document.  Please review these documents
   carefully, as they describe your rights and restrictions with respect
   to this document.  Code Components extracted from this document must
   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Scenario and Background . . . . . . . . . . . . . . . . . . .   3
   3.  Methodology . . . . . . . . . . . . . . . . . . . . . . . . .   5
     3.1.  Data collecting . . . . . . . . . . . . . . . . . . . . .   5
     3.2.  Sending data  . . . . . . . . . . . . . . . . . . . . . .   7
     3.3.  Preprocessing . . . . . . . . . . . . . . . . . . . . . .   7
     3.4.  Results . . . . . . . . . . . . . . . . . . . . . . . . .   7
   4.  Security Considerations . . . . . . . . . . . . . . . . . . .   9
   5.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .   9
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   9
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   9
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .   9
     7.2.  Informative References  . . . . . . . . . . . . . . . . .   9
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  10

1.  Introduction

   This document describes a scenario and a methodology that can be used
   to get performance details from a monitored network.  The approach is
   inspired by the concepts illustrated in the Alternate Marking Method
   (RFC 8321 [RFC8321]), Multipoint Alternate Marking Method
   ([I-D.ietf-ippm-multipoint-alt-mark]), and Hash Sampling (RFC 5474
   [RFC5474] and RFC 5475 [RFC5475]).

   In general the performance measurement results are based on a
   posteriori calculation and the method is called Big Data Multipoint
   Alternate Marking performance measurement.

   The kinds of measurements are specified on the Network Management
   System (NMS) and they can be split into two main categories: per
   cluster and end-to-end.



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   o  The per cluster approach includes all the details that refer to
      each single cluster and provides a list of parameters that
      characterize it (packet loss, mean delay).

   o  The end-to-end approach provides more general information about
      the entire path (packet loss, mean delay).

   The results can be provided on demand, in a non real-time processing
   environment, and each one of them refers to a single monitoring
   period, even if it is possible to broaden the search to more periods.

   The basic mechanism of the Big data approach here introduced is the
   Packet sampling.  Packet sampling, which is performed through Hashing
   Sampling technique (RFC 5474 [RFC5474] and RFC 5475 [RFC5475])
   applied on all incoming traffic, without any flow distinction.
   Nevertheless, thanks to data postprocessing, results are split by
   flow afterwards, since the storage system memorizes the fields of the
   packet headers that identify flows.  The NMS, in fact, requires, as
   input parameters, the flow identification fields as well as the
   timestamp.

   The use of hash sampling improves packet tracking performance and
   thus overall performance.  It allows to track the path followed by
   each packet without further efforts by the NMS.

2.  Scenario and Background

   The service provider's network is made up of a main backbone network
   surrounded by routers that handle customers traffic input and output.
   The proposed methodology requires that the traffic is marked before
   entering the backbone network, by means of the Alternate Marking
   technique.  The marking process can be made by the edge routers or by
   the customers itself, keeping in mind that it requires that the
   markers are synchronized.

















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                            ,..-..,_
                         .'`        '.
                 +--+  ,'             `.  +--+
             ----|R1| /     Backbone    \ |R2|----
                 +--+/      Network      \+--+
     [unmarked       |                   |       [unmarked
      traffic]       |      [marked      |        traffic]
                 +--+\      traffic]     /+--+
             ----|R3| \                 / |R4|----
                 +--+  `,             ,'  +--+
                         '.        ,'`
                           `''-''``


                        Figure 1: Backbone Network

   Only the marked traffic can be monitored.  It is possible to mark
   traffic partially; the results will not be affected by unmarked
   packets and will refer only to the marked ones.  In order to do that
   a time reference period and a marking method must be fixed at the
   beginning.  The time reference period must consider the misalignment
   between the marking source routers, clock error between network
   devices and the interval we need to wait to avoid packets being out
   of order because of network delay, as described in RFC 8321 [RFC8321]
   and [I-D.ietf-ippm-multipoint-alt-mark].

   A possible marking method could use two bits of the header and set
   them to 0x01, to identify a period, and to 0x10 to identify the next
   one.  This allows to distinguish between marked traffic and unmarked
   traffic, instead of using just one bit, which can can generate
   misunderstanding between the unmarked traffic (that has the marked
   bit set to 0 by default) and the marked traffic (that alternates
   between 0x0 and 0x1, with 0x0 as marker value and not as default).
   As an alternative, it is possible to use just one bit to mark, and
   use a filter based on IP address to distinguish the flow to be
   monitored from the others.

   To enable the Big Data approach for monitoring, the network nodes
   require a packet collector, that is the agent installed on board of
   the network node that collects measurements, based on the configured
   Packet sampling criteria.

   The portion of network to be monitored must be delimited by routers
   with packet collector installed on.  The rest of the network cannot
   be monitored even if the traffic is marked.  So, the size of the
   monitored network depends on the network devices placement.  However,
   the size of the network surrounded by packet collectors must be less
   than or equal to the size of the network with marked traffic.



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3.  Methodology

   The method described here consists of the following steps:

   1.  Data collecting;

   2.  Sending data;

   3.  Preprocessing;

   4.  Results.

         ___________            _____________
        |  Packet   |          |             |
        |collector 1|  ----->  |             |
        |___________|          |Preprocessing|
                               |             |
                               |             |      ___________
         ___________           |             |     |           |
        |  Packet   |          | (Grouping)  |     | Clusters  |
        |collector 2|  ------> |             | <-- |information|
        |___________|          |             |     |           |
                               |             |     |___________|
                               |   Results   |
         ___________           |             |
        |  Packet   |          |             |
        |collector 3| -------> |             |
        |___________|          |_____________|


                   Figure 2: Outline of the Methodology

3.1.  Data collecting

   The Data collecting phase implies that, on board of the network
   nodes, the packet collector analyses data passing through a network
   interface.  A packet collector needs to be placed into each router
   interface we want to monitor.

   The agent is configured by setting:

   o  the reference hash,

   o  the maximum number of packets to store,

   o  the alternate marking period duration,

   o  the two alternate marking values that identify the marked flow,



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   o  the interface to monitor,

   o  the flow to monitor (i.e identified by header IP fields).

   We can select to monitor either a subset (e.g. filter on a particular
   IP source or destination address range) or all the flows (filter
   off).  After that, the packet collector will only take care to verify
   consistency between filter and identification fields and no more
   information about flow will be carried out.

   The Data collection is based on Hashing sampling described in RFC
   5474 [RFC5474] and RFC 5475 [RFC5475].

   The process is recursive.  Each incoming packet is hashed, compared
   with the reference hash, and recorded if the number of bits that are
   matching are the same of those required by the packet collector .
   When the number of matched packets exceed the maximum number
   requested in configuration, the number of bits to match is increased
   by one.  At this point all the previous stored packets could be
   potentially discarded and must be rechecked.  So data are stored
   temporally and are subject to changes, discards and additions.  After
   the period ends, previous data are still subject to change, but after
   a guard band (reasonably L/2 if L is the period duration) data are
   stored permanently and ready to be sent.

   Note that the packet collector (carried out with probe) selects the
   packets based on the configured parameters, so it works with every
   incoming packet, without distinction.  This greatly increases the
   probe configuration easiness.  Otherwise the probe should save all
   possible flows (potentially too many), which would be too expensive
   for the device, and need to be reconfigured if a new flow is
   available for performance monitoring.  On the other hand, this
   increases the amount of data collected.

   Stored data include two kind of details: one refers to each single
   packet and the other one is about aggregate measures.

      The first set of data includes the fields that identify the flow
      (IP header fields), packet hash, timestamp when the packets come
      in, period to which data refer.

      The second set of data reports network interface identification,
      total counted packets, total hashed packets, mean timestamp based
      on all the timestamp of all packets that passed through the
      interface, period.






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3.2.  Sending data

   The Sending data phase is separated from the previous one.  Once the
   data has been stored and collected as logs by the network device
   following the provisions of the theoretical model, the sending system
   has only the task of carrying data safely and reliably.  It is
   possible to use a synchronous mechanism, in which the sending system
   periodically checks the availability of new data, or an asynchronous
   mechanism.  In the last case when a new batch of data is ready, an
   alert wakes up the sending system that carries them to the
   destination.

3.3.  Preprocessing

   The Preprocessing phase has two main goals:

   o  aggregate input data to produce a new record that is ready to be
      postprocessed and that makes it easier to obtain performance
      parameters;

   o  decrease the total amount of data to store.

   Although this step is not mandatory, it is recommended to speed up
   subsequent operations and to give a better shape to the stored data
   in order to fit well with the last queries.

   Preprocessing can be done after data has been stored into the NMS in
   an iterative loop that parses that periodically or just before to be
   sent to NMS, through a consolidator, that collects data that comes
   from all network devices, parses them and then sends them to the NMS.

   However, in this phase it is possible to group incoming data from all
   devices and determine the path followed by each sampled packet.  In
   order to do that, if the data are grouped by hash and ordered them by
   timestamp, it is possible to outline the path.

   After providing to the NMS the topology information and Clusters
   partition of the monitored network, it is also possible to track the
   crossed cluster for each couple of sorted data, by analyzing the
   interface ID available in the stored record and comparing them with
   the edge that characterizes the clusters available in the monitored
   network.

3.4.  Results

   The Results phase involves the preprocessed records lay into
   database.  When necessary the storage system can be queried, in a




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   deferred time.  The records are organized to fit well with the
   queries that care about timing and loss aspects.

   Results are achieved by querying the storage system properly.
   Certainly, input parameters that identify which flow we are
   addressing are required.  Additionally, time reference is needed to
   select only the packets of interest.  The Big data system is aware of
   flow identification fields and performs packet flow grouping on the
   fly.  The results described below can refer to different flows,
   depending on which parameters have been specified for the query.

   It is possible to deduce the cluster mean delay D_i (mean delay
   referred to cluster i), by analyzing each record, computing delay d_j
   (delay referred to record j) as difference between the two available
   timestamps, that correspond to the input timestamp (when the packet
   has gone into the cluster) and the output timestamp (when the packet
   has gone out of that cluster), and summing it with all other delays;
   then the result is divided by the number of records that refer to the
   same cluster:

   D_i = [d_0 + d_1 +...+ d_(N_i-1)] / N_i

   Where D_i is the mean delay related to cluster i, d_j the delay
   related to record j, N_i the total number of records belonging to
   cluster i.

   It could also be computed the end-to-end mean delay AD as the sum of
   all delays available in our database, and dividing it by all the
   records:

   AD = [ad_0 + ad_1 +...+ ad_(M-1)] / M

   Where AD is the end-to-end mean delay, ad_j the delay related to
   records j, and M the total number of records.

   If necessary, after observing an unusual cluster delay, it could be
   possible to compute also max/avg/min link delay, by analyzing records
   again, and exploiting the difference between the two timestamps.

   Additionally, also details about loss are available.  Since the total
   packets are counted by each node, the sum of the input packets must
   be equal to the sum of the output packets inside each cluster.  If
   their difference is greater than 0, then a loss has occurred, and the
   result is the total loss.  The total packet loss per cluster:

   PL_i = [p_(i,0) + p_(i,1) +...+ p_(i,K-1)] - [p_(o,0) + p_(o,1) +...+
   p_(o,L-1)]




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   Considering cluster i with K input nodes and L output nodes, the
   calculation follows [I-D.ietf-ippm-multipoint-alt-mark].

   In the same way it is possible to get the entire packet loss, as the
   sum of all the packet loss per cluster.  The same measure can be
   obtained by using only the hashed packets, but in this case, we get
   an approximate measurement that might reflect or not the real one.

   Notice that all these measurements refers to the flow we specify as
   input of the query and that the specified flow can include or not all
   the sampled packets (e.g. filter on ip_src=0.0.0.0/0,
   ip_dst=0.0.0.0/0, port_src=/, port_dst=/, type=tcp, outlines a flow
   that includes all the TCP packets in an IP network).

4.  Security Considerations

   tbc

5.  Acknowledgements

   The authors would like to thank Guido Marchetto for the precious
   contribution.

6.  IANA Considerations

   tbc

7.  References

7.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC8321]  Fioccola, G., Ed., Capello, A., Cociglio, M., Castaldelli,
              L., Chen, M., Zheng, L., Mirsky, G., and T. Mizrahi,
              "Alternate-Marking Method for Passive and Hybrid
              Performance Monitoring", RFC 8321, DOI 10.17487/RFC8321,
              January 2018, <https://www.rfc-editor.org/info/rfc8321>.

7.2.  Informative References








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   [I-D.ietf-ippm-multipoint-alt-mark]
              Fioccola, G., Cociglio, M., Sapio, A., and R. Sisto,
              "Multipoint Alternate Marking method for passive and
              hybrid performance monitoring", draft-ietf-ippm-
              multipoint-alt-mark-06 (work in progress), February 2020.

   [RFC5474]  Duffield, N., Ed., Chiou, D., Claise, B., Greenberg, A.,
              Grossglauser, M., and J. Rexford, "A Framework for Packet
              Selection and Reporting", RFC 5474, DOI 10.17487/RFC5474,
              March 2009, <https://www.rfc-editor.org/info/rfc5474>.

   [RFC5475]  Zseby, T., Molina, M., Duffield, N., Niccolini, S., and F.
              Raspall, "Sampling and Filtering Techniques for IP Packet
              Selection", RFC 5475, DOI 10.17487/RFC5475, March 2009,
              <https://www.rfc-editor.org/info/rfc5475>.

Authors' Addresses

   Mauro Cociglio
   Telecom Italia
   Via Reiss Romoli, 274
   Torino  10148
   Italy

   Email: mauro.cociglio@telecomitalia.it


   Calogero Corbo
   Politecnico di Torino

   Email: corbocalo94@gmail.com


   Giuseppe Fioccola
   Huawei Technologies
   Riesstrasse, 25
   Munich  80992
   Germany

   Email: giuseppe.fioccola@huawei.com











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   Massimo Nilo
   Telecom Italia
   Via Reiss Romoli, 274
   Torino  10148
   Italy

   Email: massimo.nilo@telecomitalia.it


   Riccardo Sisto
   Politecnico di Torino
   Corso Duca degli Abruzzi, 24
   Torino  10129
   Italy

   Email: riccardo.sisto@polito.it



































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